code: def test_eval_results_file():
      eval_results_file = create_eval_results_json(
          prompt_parameters,
          model_input,
          model_output_parameters,
          model_output,
      )
      expected_eval_results_json = {
          "columns": [
              "question",
              "context",
              "prompt",
              "output",
              "tokens",
              "latency",
          ],
          "data": [
              [
                  "my_question",
                  "my_context",
                  "answer this question: my_question using the following context: my_context",
                  "my_answer",
                  "10",
                  "100",
              ]
          ],
      }
      assert json.loads(eval_results_file) == expected_eval_results_json
  
file_path: mlflow/tests/utils/test_promptlab_utils.py
imports: import json
  import os
  import pytest
  from mlflow.entities.param import Param
  from mlflow.entities.run_status import RunStatus
  from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
  from mlflow.store.tracking.file_store import FileStore
  from mlflow.utils.promptlab_utils import _create_promptlab_run_impl, create_eval_results_json
label_desc: Use image node_id 1 for calling a global function with example usage: test_eval_results_file() without return types